Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: Jun 17, 2024
Date Accepted: Sep 5, 2024
EDAI framework for Integrating Equity, Diversity, and Inclusion throughout the Lifecycle of Artificial Intelligence to Improve Health and Oral Health Care: A Qualitative Study
ABSTRACT
Background:
Recent studies have identified significant gaps in equity, diversity, and inclusion (EDI) considerations within the lifecycle of Artificial Intelligenec (AI), spanning from data collection and problem definition to implementation stages. Despite the recognized need for integrating EDI principles, there is currently no existing guideline or framework to support this integration in AI lifecycle.
Objective:
This study aimed to address this gap by identifying EDI principles and indicators to be integrated into the AI lifecycle. The goal was to develop a comprehensive guiding framework to guide the development and implementation of future AI systems.
Methods:
This study followed a qualitative exploratory design using a constructivist grounded theory methodology. The research was conducted in three phases: (1) a comprehensive scoping review explored how EDI principles have been integrated into AI in health and oral healthcare settings and identified relevant EDI indicators. (2) a multidisciplinary team was established, and a 2-day in-person international workshop with 40 representatives from diverse backgrounds, expertise and communites was conducted. The workshop included plenary presentations, round table discussions, and focused group discussions. (3) Based on workshop insights, the EDAI framework was developed and refined through iterative feedback from participants.
Results:
The study resulted in the co-designed EDAI framework, which includes a detailed compilation of EDI indicators across the AI lifecycle, addressing individual, organizational, and system levels.
Conclusions:
The insights from this study have the potential to reshape perspectives on integrating EDI into AI lifecycle. The EDAI framework provides a robust guideline to support the development and implementation of AI systems that uphold equity, diversity, and inclusion, ultimately benefiting diverse stakeholders and promoting ethical and responsible AI development and implementation in health and oral health care.
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